A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications

doi:10.1109/TVT.2015.2509465

CASIA OpenIR > 复杂系统管理与控制国家重点实验室 > 先进控制与自动化

	A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications
	Wang, Kunfeng1 ; Liu, Yuqiang1,2 ; Gou, Chao1,3 ; Wang, Fei-Yue1,4 ; Wang, Kunfeng(王坤峰)
	2016-06-01
发表期刊	IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
卷号	65 期号:6 页码:4144-4158
文章类型	Article
摘要	This paper proposes an effective multi-view learning approach to foreground detection for traffic surveillance applications. This approach involves three main steps. First, a reference background image is generated via temporal median filtering, and multiple heterogeneous features (including brightness variation, chromaticity variation, and texture variation, each of which represents a unique view) are extracted from the video sequence. Then, a multi-view learning strategy is devised to online estimate the conditional probability densities for both the foreground and the background. The probability densities of three features are approximately conditionally independent and are estimated with kernel density estimation. Pixel soft labeling is conducted by using Bayes rule, and the pixelwise foreground posteriors are computed. Finally, a Markov random field is constructed to incorporate the spatiotemporal context into the foreground/background decision model. The belief propagation algorithm is used to label each pixel of the current frame. Experimental results verify that the proposed approach is effective to detect foreground objects from challenging traffic environments and outperforms some state-of-the-art methods.
关键词	Conditional Independence Foreground Detection Heterogeneous Features Markov Random Field (Mrf) Multi-view Learning
WOS标题词	Science & Technology ; Technology
学科领域	Civil Engineering
DOI	10.1109/TVT.2015.2509465
关键词[WOS]	GAUSSIAN MIXTURE MODEL ; REAL-TIME TRACKING ; BACKGROUND SUBTRACTION ; OBJECT DETECTION ; VISUAL SURVEILLANCE ; CAST SHADOWS ; SEGMENTATION ; SUPPRESSION ; EDGE
URL	查看原文
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61304200) ; MIIT Project of Internet of Things Development Fund(1F15E02)
WOS研究方向	Engineering ; Telecommunications ; Transportation
WOS类目	Engineering, Electrical & Electronic ; Telecommunications ; Transportation Science & Technology
WOS记录号	WOS:000380068500026
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10859
专题	复杂系统管理与控制国家重点实验室_先进控制与自动化
通讯作者	Wang, Kunfeng(王坤峰)
作者单位	1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.China Acad Railway Sci, Beijing 100081, Peoples R China 3.Qingdao Acad Intelligent Ind, Qingdao 266109, Peoples R China 4.Natl Univ Def Technol, Res Ctr Computat Expt & Parallel Syst, Changsha 410073, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,et al. A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications[J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,2016,65(6):4144-4158.
APA	Wang, Kunfeng,Liu, Yuqiang,Gou, Chao,Wang, Fei-Yue,&Wang, Kunfeng.(2016).A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications.IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY,65(6),4144-4158.
MLA	Wang, Kunfeng,et al."A Multi-view Learning Approach to Foreground Detection for Traffic Surveillance Applications".IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 65.6(2016):4144-4158.

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